This article provides a beginner-friendly explanation of attention mechanisms and transformer models, covering sequence-to-sequence modeling, the limitations of RNNs, the concept of attention, and how transformers address these limitations with self-attention and parallelization.
This Space demonstrates a simple method for embedding text using a LLM (Large Language Model) via the Hugging Face Inference API. It showcases how to convert text into numerical vector representations, useful for semantic search and similarity comparisons.
Alibaba Cloud released its Qwen2.5-Omni-7B multimodal AI model, designed for cost-effective AI agents and capable of processing various inputs like text, images, audio, and video.
This Splunk Lantern article outlines the steps to monitor Gen AI applications with Splunk Observability Cloud, covering setup with OpenTelemetry, NVIDIA GPU metrics, Python instrumentation, and OpenLIT integration to monitor GenAI applications built with technologies like Python, LLMs (OpenAI's GPT-4o, Anthropic's Claude 3.5 Haiku, Meta’s Llama), NVIDIA GPUs, Langchain, and vector databases (Pinecone, Chroma) using Splunk Observability Cloud. It outlines a six-step process:
- Access Splunk Observability Cloud: Sign up for a free trial if needed.
- Deploy Splunk Distribution of OpenTelemetry Collector: Use a Helm chart to install the collector in Kubernetes.
- Capture NVIDIA GPU Metrics: Utilize the NVIDIA GPU Operator and Prometheus receiver in the OpenTelemetry Collector.
- Instrument Python Applications: Use the Splunk Distribution of OpenTelemetry Python agent for automatic instrumentation and enable Always On Profiling.
- Enhance with OpenLIT: Install and initialize OpenLIT to capture detailed trace data, including LLM calls and interactions with vector databases (with options to disable PII capture).
- Start Using the Data: Leverage the collected metrics and traces, including features like Tag Spotlight, to identify and resolve performance issues (example given: OpenAI rate limits).
The article emphasizes OpenTelemetry's role in GenAI observability and highlights how Splunk Observability Cloud facilitates monitoring these complex applications, providing insights into performance, cost, and potential bottlenecks. It also points to resources for help and further information on specific aspects of the process.
A review of the Qwen2.5-VL-32B large language model, noting its performance, capabilities, and how it runs on a 64GB Mac. Includes a demonstration with a map image and performance statistics.
NVIDIA DGX Spark is a desktop-friendly AI supercomputer powered by the NVIDIA GB10 Grace Blackwell Superchip, delivering 1000 AI TOPS of performance with 128GB of memory. It is designed for prototyping, fine-tuning, and inference of large AI models.
Mistral Small 3.1 is a cutting-edge, open-source AI model released by Mistral AI, designed for efficiency and excelling in multimodal and multilingual tasks. It supports a 128k token context window and is optimized for real-time conversational AI and domain-specific fine-tuning.
A new paper by researchers from Google Research and UC Berkeley shows that a simple sampling-based search approach can enhance the reasoning abilities of large language models (LLMs) without needing specialized training or complex architectures.
This article explores the Model Context Protocol (MCP), an open protocol designed to standardize AI interaction with tools and data, addressing the fragmentation in AI agent ecosystems. It details current use cases, future possibilities, and challenges in adopting MCP.
This study demonstrates that neural activity in the human brain aligns linearly with the internal contextual embeddings of speech and language within large language models (LLMs) as they process everyday conversations.
